Sensitivity statistics for instrumental variable estimates
Source:R/sensitivity_stats.R
sensitivity_stats.RdConvenience function that computes robustness values for IV estimates as well as auxiliary first stage and reduced form regressions.
Usage
sensitivity_stats(...)
# S3 method for class 'iv_fit'
sensitivity_stats(model, parm = "iv", q = 1, alpha = 0.05, min = TRUE, ...)
# S3 method for class 'iv.sensemakr'
sensitivity_stats(model, parm = "iv", q = 1, alpha = 0.05, min = TRUE, ...)Arguments
- ...
further arguments passed to or from other methods.
- model
a model created with the function
iv_fit.- parm
contour plots of which estimate? Options are
ivfor instrumental variable estimates,fsfor first-stage estimates, andrffor reduced-form estimates.- q
percent change of the effect estimate that would be deemed problematic. Default is 1, which means a reduction of 100% of the current effect estimate (bring estimate to zero).
- alpha
significance level.
- min
should we consider biases as large or larger than a certain amount? Default is
TRUE.
Value
A data.frame with columns for the estimate, confidence interval
bounds (lower and upper), t-value, extreme robustness value (xrv_qa),
robustness value (rv_qa), and the parameters used (q, min,
alpha, dof).
Examples
data("card")
y <- card$lwage
d <- card$educ
z <- card$nearc4
x <- model.matrix( ~ exper + expersq + black + south + smsa + reg661 + reg662 +
reg663 + reg664 + reg665+ reg666 + reg667 + reg668 + smsa66,
data = card)
card.fit <- iv_fit(y, d, z, x)
# sensitivity statistics for the IV estimate
sensitivity_stats(card.fit)
#> estimate lwr upr t.value xrv_qa rv_qa q min alpha
#> iv 0.1315038 0.02480484 0.2848236 2.327075 0.0005232443 0.006666407 1 1 0.05
#> dof
#> iv 2994
# sensitivity statistics for the first-stage
sensitivity_stats(card.fit, parm = "fs")
#> estimate lwr upr t.value xrv_qa rv_qa q min alpha
#> fs 0.3198989 0.1476194 0.4921785 3.64085 0.003129076 0.03023129 1 1 0.05
#> dof
#> fs 2994